Table 4. Relationship between Sentiment, Twitter user and URL author sector and evidence quality, n = 726.
Tweet variable | Code | Cites peer-reviewed research | Does not cite peer-reviewed research | All | Standardised residuals (z scores)* | Overall significance |
---|---|---|---|---|---|---|
URL author sector | Health sector | 95 | 33 | 128 | z = ±4.6 | χ2 = 67.6, df = 2, p<0.001 |
Tobacco industry-linked | 8 | 42 | 50 | z = ±2.7 | ||
Neither | 217 | 331 | 548 | z = ±1.4 | ||
Twitter user sector |
Health sector | 71 | 45 | 116 | z = ±2.5 | χ2 = 22.6, df = 2, p<0.001 |
Tobacco industry-linked | 3 | 18 | 21 | z = ±1.8 | ||
Neither | 246 | 343 | 589 | z = ±0.8 | ||
Sentiment | Positive | 286 | 116 | 402 | z = ±7.3 | χ2 = 272.2, df = 3, p<0.001 |
Negative | 13 | 98 | 111 | z = ±4.6 | ||
Neutral | 8 | 143 | 151 | z = ±6.4 | ||
Unclear | 13 | 49 | 62 | z = ±2.4 |
* Categories which significantly contribute to the overall chi squared statistic have z scores outside ±1.96 (significant at p<0.05), outside ±2.58 (significant at p<0.01), and outside ±3.29 (significant at p<0.001). All significant scores are highlighted in bold.